glmc is a collection of functions to
fit generalized linear models where the parameters are subject to linear
constraints. The model is specified by giving a symbolic description of the
linear predictor, a description of the error distribution, and a matrix of
constraints on the parameters.
For a complete list of the functions, use library(help="glmc")
or read the rest of the manual. For a simple demonstration,
use demo(packages="glmc").
When publishing results obtained using this package the original authors are to be cited as:
Mark S. Handcock, Sanjay Chaudhuri, and Michael S. Rendall. 2004 glmc: An R package for generalized linear models subject to constraints.
All programs derived from this package must cite it.
For complete citation information, use
citation(package="glmc").
In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates.
Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are nonlinear making the task of maximum likelihood estimation harder. In this package we provide an alternative approach exploiting the notion of an empirical likelihood. Within the framework of generalised linear models, the population level information corresponds to linear constraints, which are comparatively easy to handle. We provide a two-step algorithm that produces parameter estimates using only unconstrained estimation. We also provide computable expressions for the standard errors.
Sanjay Chaudhuri, Mark S. Handcock, and Michael S. Rendall. 2004 Generalised Linear Models Incorporating Population Level Information: An Empirical Likelihood Based Approach, Working Paper, Center for Statistics and the Social Sciences, University of Washington.